Machine Learning-Based App for Self-Evaluation of Teacher-Specific Instructional Style and Tools
Machine Learning-Based App for Self-Evaluation of Teacher-Specific Instructional Style and Tools
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DOI：
10.3390/educsci8010007
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发表时间：
2018-03-01
影响因子：
3
通讯作者：
Gustafsson, Anders
中科院分区：
文献类型：
--
作者：
Duzhin, Fedor;Gustafsson, Anders
课程讲师需要评估他们的教学方法的有效性，但教育实验在政治上、管理上或伦理上很少是可行的。另一方面，准实验工具往往是有问题的，因为它们通常太复杂，不能被教育工作者广泛使用，而且可能会因为混淆的变量(如学生的先前知识)而产生选择偏差。我们开发了一种机器学习算法，它考虑了学生的先验知识。我们的算法是基于符号回归的，它使用大学收集的先前分数的非实验数据作为输入。它可以预测学生考试成绩60%-70%的变化。将我们的算法应用到常微分方程式课堂教学效果的评估中，我们发现，与传统的手写作业相比，点击器是一种更有效的教学策略；然而，具有即时反馈的在线作业被发现比点击器更有效。我们发现的新奇之处在于方法(基于机器学习的非实验数据分析)，以及我们比较了点击器和手写作业在本科数学教学中的有效性。评估微积分课上使用的方法，我们发现积极的团队合作似乎比个人工作更有利于学生。我们的算法已经集成到一个应用程序中，我们正在与教育社区分享，所以它可以被从业者使用，而不需要高级的方法论培训。
Course instructors need to assess the efficacy of their teaching methods, but experiments in education are seldom politically, administratively, or ethically feasible. Quasi-experimental tools, on the other hand, are often problematic, as they are typically too complicated to be of widespread use to educators and may suffer from selection bias occurring due to confounding variables such as students' prior knowledge. We developed a machine learning algorithm that accounts for students' prior knowledge. Our algorithm is based on symbolic regression that uses non-experimental data on previous scores collected by the university as input. It can predict 60-70 percent of variation in students' exam scores. Applying our algorithm to evaluate the impact of teaching methods in an ordinary differential equations class, we found that clickers were a more effective teaching strategy as compared to traditional handwritten homework; however, online homework with immediate feedback was found to be even more effective than clickers. The novelty of our findings is in the method (machine learning-based analysis of non-experimental data) and in the fact that we compare the effectiveness of clickers and handwritten homework in teaching undergraduate mathematics. Evaluating the methods used in a calculus class, we found that active team work seemed to be more beneficial for students than individual work. Our algorithm has been integrated into an app that we are sharing with the educational community, so it can be used by practitioners without advanced methodological training.